experiments/analysis/data_statistics_figure.py

"""Generate dataset statistics figure from the currently-available annotations.

Panels (3):
    (a) Recording duration distribution per scene (boxplot)
    (b) Segment length distribution (histogram)
    (c) Top-20 manipulated objects by segment count

Note: panel for motor-primitive frequency is deferred until the 18-primitive
annotation pipeline (anno.py) is rerun across all recordings.
"""
import json, re
from pathlib import Path
from collections import Counter
import numpy as np
import matplotlib.pyplot as plt

ANNO_DIR = Path("${PULSE_ROOT}/annotations_by_scene")
OUT = Path("${PULSE_ROOT}/paper/figures/dataset_stats.pdf")

# Chinese -> English object name mapping (from anno.py OBJECT_TRANSLATIONS)
OBJ_EN = {
    "笔记本电脑": "laptop", "有线鼠标": "wired mouse", "有线键盘": "wired keyboard",
    "马克笔": "marker", "胶带": "tape", "笔记本电源": "laptop power", "折叠伞": "umbrella",
    "剪刀": "scissors", "钱包": "wallet", "纸": "paper", "订书机": "stapler",
    "纸箱": "box", "文件": "document", "架子": "rack", "桌布": "tablecloth", "罐子": "jar",
    "调料瓶": "seasoning bottle", "密封罐": "sealed jar", "厨房纸巾": "kitchen paper",
    "抹布": "cloth", "茶包": "tea bag", "饭碗": "rice bowl", "菜盘": "plate",
    "菜锅": "pot", "勺子": "spoon", "水杯": "water cup", "茶杯": "tea cup",
    "茶壶": "teapot", "食物残渣": "food residue", "垃圾桶": "trash bin",
    "纸巾": "tissue", "餐垫": "placemat", "托盘": "tray", "清洁喷雾": "spray",
    "食物": "food", "电源": "power adapter", "移动硬盘": "HDD", "鼠标": "mouse",
    "笔记本充电器": "laptop charger", "转换插头": "plug adapter", "插线板": "power strip",
    "线材收纳包": "cable organizer", "衬衫": "shirt", "裤子": "pants",
    "牙膏": "toothpaste", "牙刷": "toothbrush", "牙刷盒": "toothbrush case",
    "剃须刀": "razor", "毛巾": "towel", "皮鞋": "shoes", "鞋袋": "shoe bag",
    "耳机": "headphones", "护照套": "passport holder", "证件夹": "ID holder",
    "纸巾包": "tissue pack", "行李箱": "suitcase", "马克杯": "mug",
    "调料罐": "seasoning jar", "茶罐": "tea canister", "外套": "coat",
    "围巾": "scarf", "衣架": "hanger",
}


def parse_t(ts: str) -> float:
    parts = ts.split(":")
    if len(parts) == 2:  # MM:SS
        m, s = parts
        return int(m) * 60 + int(s)
    h, m, s = parts
    return int(h) * 3600 + int(m) * 60 + int(s)


durations = {f"S{i}": [] for i in range(1, 9)}
seg_lengths = []
objects = Counter()

for v_dir in sorted(ANNO_DIR.glob("v*")):
    for jf in sorted(v_dir.glob("s*.json")):
        scene = jf.stem.upper()
        try:
            data = json.loads(jf.read_text())
        except Exception:
            continue
        segs = data.get("segments", [])
        if not segs:
            continue
        max_end = 0
        for seg in segs:
            ts = seg.get("timestamp", "")
            if "-" not in ts:
                continue
            try:
                start, end = ts.split("-")
                s_sec, e_sec = parse_t(start), parse_t(end)
                seg_lengths.append(e_sec - s_sec)
                max_end = max(max_end, e_sec)
                for o in seg.get("objects", []) or []:
                    nm = o.get("name") if isinstance(o, dict) else o
                    if nm:
                        objects[OBJ_EN.get(nm, nm)] += 1
            except Exception:
                continue
        if max_end > 0 and scene in durations:
            durations[scene].append(max_end / 60.0)

print(f"Per-scene durations: { {s: len(v) for s, v in durations.items()} }")
print(f"Total segments: {len(seg_lengths)}")
print(f"Unique objects: {len(objects)}")
top_obj = objects.most_common(5)
print(f"Top objects: {top_obj}")

fig, axes = plt.subplots(1, 3, figsize=(12, 3.5))

# (a) Duration boxplot per scene
ax = axes[0]
scene_order = [f"S{i}" for i in range(1, 9)]
data = [durations[s] for s in scene_order]
ax.boxplot(data, tick_labels=scene_order, showfliers=False, patch_artist=True,
           boxprops=dict(facecolor="#b3cde3"))
ax.set_ylabel("Recording duration (min)")
ax.set_title("(a) Recording duration per scene")
ax.grid(axis="y", alpha=0.3)

# (b) Segment length histogram
ax = axes[1]
seg_arr = np.array(seg_lengths)
seg_arr = seg_arr[seg_arr <= 10]
ax.hist(seg_arr, bins=np.arange(0, 11) - 0.5, color="#8c96c6", edgecolor="black")
ax.set_xlabel("Segment length (s)")
ax.set_ylabel("Segment count")
ax.set_title(f"(b) Segment length (n={len(seg_lengths)})")
ax.set_xticks(range(0, 11))
ax.grid(axis="y", alpha=0.3)

# (c) Top-20 objects
ax = axes[2]
objs, ocounts = zip(*objects.most_common(20))
ax.barh(objs[::-1], ocounts[::-1], color="#74c476")
ax.set_xlabel("Segment count")
ax.set_title("(c) Top-20 manipulated objects")
ax.tick_params(axis="y", labelsize=8)
ax.grid(axis="x", alpha=0.3)

fig.tight_layout()
fig.savefig(OUT, bbox_inches="tight")
fig.savefig(str(OUT).replace(".pdf", ".png"), dpi=140, bbox_inches="tight")
print(f"Saved: {OUT}")